FengWu-W2S
# Model Introduction FengWu-W2S (FengWu Weather-to-Subseasonal) is a seamless global weather-to-subseasonal forecasting model extending FengWu. Paper: FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere https://arxiv.org/abs/2411.10191 # Model Description The model uses a six-hour time step for autoregressive forecasts of up to 42 days. Coupled atmospheric, ocean, and land branches, together with diversity perturbations, are used to improve extended-range forecast skill. This repository contains a compact implementation of the coupled interfaces for reproducible workflow checks. # Use Cases | Scenario | Description | | :---: | :--- | | Weather-to-subseasonal forecast research | Train a 78-channel coupled model with six-hourly global atmospheric, ocean, and land fields. | | Local quick validation | Use small-grid synthetic HDF5 data to check training, fine-tuning, inference, and forecast visualization. | | ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. | | Multi-GPU training | Launch PyTorch DistributedDataParallel with `torchrun`. | # Usage Guide ## 1. OneCode Usage Experience intelligent one-click AI4S programming through the OneCode online environment: [Click to Experience Intelligent One-Click AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) ## 2. Manual Installation and Usage **Hardware Requirements** - A GPU or DCU is recommended. - CPU can be used for import and small-scale connectivity verification; full training and inference will be slow. - DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching your cluster, is recommended. ### Download the Model Package ```bash hf download OneScience-Group/FengWu-W2S --local-dir ./FengWu-W2S cd FengWu-W2S ``` ### Install the Runtime Environment **DCU Environment** ```bash # Please activate DTK and CONDA first conda create -n onescience311 python=3.11 -y conda activate onescience311 # uv installation is supported pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai ``` **GPU Environment** ```bash # Please activate CONDA first conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 conda activate onescience311 # uv installation is supported pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai ``` ### Training Data Introduction The OneScience community provides an ERA5 data slice for training. Download it and confirm that the data path in `conf/config.yaml` is correct: ```bash hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data ``` Real files must contain all 78 channels listed in `conf/config.yaml`, including atmospheric, ocean, and land variables, with six-hour time spacing. The configured group indices define the coupled branches. ### Generate Synthetic Data The default configuration describes a full `721x1440` grid. For a practical local smoke test, generate a small fixture explicitly: ```bash python scripts/fake_data.py --height 32 --width 64 --timesteps 12 ``` Synthetic HDF5 files are for workflow validation only and do not represent the ERA5 reanalysis or the paper's forecast quality. ### Training Single GPU: ```bash python scripts/train.py ``` Multi-GPU: ```bash torchrun --nproc_per_node=2 scripts/train.py ``` For a short smoke run, reduce the workload while keeping the generated data and model grid consistent: ```bash python scripts/train.py --max-epoch 1 --batch-size 1 --num-workers 0 --rollout-steps 1 ``` The default checkpoint is saved to `data/checkpoints/model_bak.pth`. ### Fine-tuning Resume from the base checkpoint with the lower fine-tuning learning rate: ```bash python scripts/train.py --finetune ``` ### Training Weights This repository provides a `weight/` directory for FengWu-W2S checkpoints. The weight files will be uploaded soon and are expected to be available in the near future. ### Inference Inference reads `data/checkpoints/model_bak.pth` by default and writes forecasts grouped by initialization year to `result/output/